Towards a multimodal topic tracking system for a mobile robot

Author(s):  
Jan F. Maas ◽  
Britta Wrede ◽  
Gerhard Sagerer
2021 ◽  
Vol 33 (1) ◽  
pp. 33-43
Author(s):  
Kazuhiro Funato ◽  
Ryosuke Tasaki ◽  
Hiroto Sakurai ◽  
Kazuhiko Terashima ◽  
◽  
...  

The authors have been developing a mobile robot to assist doctors in hospitals in managing medical tools and patient electronic medical records. The robot tracks behind a mobile medical worker while maintaining a constant distance from the worker. However, it was difficult to detect objects in the sensor’s invisible region, called occlusion. In this study, we propose a sensor fusion method to estimate the position of a robot tracking target indirectly by an inertial measurement unit (IMU) in addition to the direct measurement by an laser range finder (LRF) and develop a human tracking system to avoid occlusion by a mobile robot. Based on this, we perform detailed experimental verification of tracking a specified person to verify the validity of the proposed method.


2004 ◽  
Vol 37 (8) ◽  
pp. 299-303
Author(s):  
Jaume Vergés-LlahÍ ◽  
Joan Aranda ◽  
Alberto Sanfeliu

2006 ◽  
Vol 19 (3) ◽  
pp. 164-171 ◽  
Author(s):  
Khoo Khyou Bun ◽  
Mitsuru Ishizuka

2015 ◽  
Vol 764-765 ◽  
pp. 680-684
Author(s):  
Kuo Lan Su ◽  
Jr Hung Guo ◽  
Kuo Hsien Hsia

The purpose of this paper is to develop an intelligent mobile robot using image processing technology. The mobile robot is composed of a visual tracking system, a loading platform, a balance control system, a PC-based controller, four ultrasonic sensors and a power system. We develop a PC based control system for image processing and path planning. The mobile robot can track a moving target and adjust the loading platform by the balance control system simultaneously. The Image processing based on OpenCV use two different tracking methods, MTLT (Match Template Learning Tracking) and TLD (Tracking, Learning and Detection), to track moving targets. The efficiencies of both methods for tracking the moving target on the mobile robot are compared in this paper. The loading platform control system uses HOLTEK Semiconductor Company's HT66F Series 8-bit microprocessor as the processor, and receives the feedback data from the FAS-A inclinometer sensor. The controller of the loading platform uses the PID control law according to the feedback signals of the inclinometer sensor, and controls the rotation speed of the platform motor to tune the balance level. Keywords— Intelligent mobile robot, Image processing, OpenCV, MTLT, TLD, HOLTEK, FAS-A inclinometer sensor, PID control.


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